{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "from matplotlib.pyplot import plot, ion, show, savefig, cla, figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions to load and process original csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this function load one .cvs (a sequence)\n",
    "def load_data(dataset, csv_folder='./NAB-known-anomaly/csv-files/'):\n",
    "    if dataset == 'ambient_temp':\n",
    "        data_file = os.path.join(csv_folder, 'ambient_temperature_system_failure.csv')\n",
    "        anomalies = ['2013-12-22 20:00:00', '2014-04-13 09:00:00']\n",
    "        t_unit = 'hour'\n",
    "    elif dataset == 'cpu_utilization':\n",
    "        data_file = os.path.join(csv_folder, 'cpu_utilization_asg_misconfiguration.csv')\n",
    "        anomalies = ['2014-07-12 02:04:00', '2014-07-14 21:44:00']\n",
    "        t_unit = '5 min'\n",
    "    elif dataset == 'ec2_request':\n",
    "        data_file = os.path.join(csv_folder, 'ec2_request_latency_system_failure.csv')\n",
    "        anomalies = ['2014-03-14 09:06:00', '2014-03-18 22:41:00', '2014-03-21 03:01:00']\n",
    "        t_unit = '5 min'\n",
    "    elif dataset == 'machine_temp':\n",
    "        data_file = os.path.join(csv_folder, 'machine_temperature_system_failure.csv')\n",
    "        anomalies = ['2013-12-11 06:00:00', '2013-12-16 17:25:00', '2014-01-28 13:55:00', '2014-02-08 14:30:00']\n",
    "        t_unit = '5 min'\n",
    "    elif dataset == 'rogue_agent_key_hold':\n",
    "        data_file = os.path.join(csv_folder, 'rogue_agent_key_hold.csv')\n",
    "        anomalies = ['2014-07-15 08:30:00', '2014-07-17 09:50:00']\n",
    "        t_unit = '5 min'\n",
    "    elif dataset == 'rogue_agent_key_updown':\n",
    "        data_file = os.path.join(csv_folder, 'rogue_agent_key_updown.csv')\n",
    "        anomalies = ['2014-07-15 04:00:00', '2014-07-17 08:50:00']\n",
    "        t_unit = '5 min'\n",
    "    elif dataset == 'nyc_taxi':\n",
    "        data_file = os.path.join(csv_folder, 'nyc_taxi.csv')\n",
    "        anomalies = ['2014-11-01 19:00:00', '2014-11-27 15:30:00', '2014-12-25 15:00:00', '2015-01-01 01:00:00', \n",
    "                     '2015-01-27 00:00:00']\n",
    "        t_unit = '30 min'\n",
    "    \n",
    "    t = []\n",
    "    readings = []\n",
    "    idx_anomaly = []\n",
    "    i = 0\n",
    "    with open(data_file) as csvfile:\n",
    "        readCSV = csv.reader(csvfile, delimiter=',')\n",
    "        print(\"\\n--> Anomalies occur at:\")\n",
    "        for row in readCSV:\n",
    "            if i > 0:\n",
    "                t.append(i)\n",
    "                readings.append(float(row[1]))\n",
    "                for j in range(len(anomalies)):\n",
    "                    if row[0] == anomalies[j]:\n",
    "                        idx_anomaly.append(i)\n",
    "                        print(\"  timestamp #{}: {}\".format(j, row[0]))\n",
    "            i = i + 1\n",
    "    t = np.asarray(t)\n",
    "    readings = np.asarray(readings)\n",
    "    print(\"\\nOriginal csv file contains {} timestamps.\".format(t.shape))\n",
    "    print(\"Processed time series contain {} readings.\".format(readings.shape))\n",
    "    print(\"Anomaly indices are {}\".format(idx_anomaly))\n",
    "    \n",
    "    return t, t_unit, readings, idx_anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function plots a dataset with the train/test split and known anomalies\n",
    "# Relies on helper function load_data()\n",
    "\n",
    "def process_and_save_specified_dataset(dataset, idx_split, y_scale=5, save_file=False):\n",
    "    t, t_unit, readings, idx_anomaly = load_data(dataset)\n",
    "    \n",
    "    # split into training and test sets\n",
    "    training = readings[idx_split[0]:idx_split[1]]\n",
    "    t_train = t[idx_split[0]:idx_split[1]]\n",
    "    \n",
    "    # normalise by training mean and std \n",
    "    train_m = np.mean(training)\n",
    "    train_std = np.std(training)\n",
    "    print(\"\\nTraining set mean is {}\".format(train_m))\n",
    "    print(\"Training set std is {}\".format(train_std))\n",
    "    readings_normalised = (readings - train_m) / train_std\n",
    "    \n",
    "    training = readings_normalised[idx_split[0]:idx_split[1]]\n",
    "    if idx_split[0] == 0:\n",
    "        test = readings_normalised[idx_split[1]:]\n",
    "        t_test = t[idx_split[1]:] - idx_split[1]\n",
    "        idx_anomaly_test = np.asarray(idx_anomaly) - idx_split[1]\n",
    "    else:\n",
    "        test = [readings_normalised[:idx_split[0]], readings_normalised[idx_split[1]:]]\n",
    "        t_test = [t[:idx_split[0]], t[idx_split[1]:] - idx_split[1]]\n",
    "        idx_anomaly_split = np.squeeze(np.argwhere(np.asarray(idx_anomaly)>idx_split[0]))\n",
    "        idx_anomaly_test = [np.asarray(idx_anomaly[:idx_anomaly_split[0]]), \n",
    "                            np.asarray(idx_anomaly[idx_anomaly_split[0]:]) - idx_split[1]]\n",
    "    print(\"Anomaly indices in the test set are {}\".format(idx_anomaly_test))\n",
    "    \n",
    "    if save_file:\n",
    "        save_dir = './datasets/NAB-known-anomaly/'\n",
    "        np.savez(save_dir+dataset+'.npz', t=t, t_unit=t_unit, readings=readings, idx_anomaly=idx_anomaly,\n",
    "                    idx_split=idx_split, training=training, test=test, train_m=train_m, train_std=train_std,\n",
    "                    t_train=t_train, t_test=t_test, idx_anomaly_test=idx_anomaly_test)\n",
    "        print(\"\\nProcessed time series are saved at {}\".format(save_dir+dataset+'.npz'))\n",
    "    else:\n",
    "        print(\"\\nProcessed time series are not saved.\")\n",
    "    \n",
    "    # plot the whole normalised sequence\n",
    "    fig, axs = plt.subplots(1, 1, figsize=(18, 4), edgecolor='k')\n",
    "    fig.subplots_adjust(hspace=.4, wspace=.4)\n",
    "    # axs = axs.ravel()\n",
    "    # for i in range(4):\n",
    "    axs.plot(t, readings_normalised)\n",
    "    if idx_split[0] == 0:\n",
    "        axs.plot(idx_split[1]*np.ones(20), np.linspace(-y_scale,y_scale,20), 'b--')\n",
    "    else:\n",
    "        for i in range(2):\n",
    "            axs.plot(idx_split[i]*np.ones(20), np.linspace(-y_scale,y_scale,20), 'b--')\n",
    "    for j in range(len(idx_anomaly)):\n",
    "        axs.plot(idx_anomaly[j]*np.ones(20), np.linspace(-y_scale,y_scale,20), 'r--')\n",
    "    #     axs.plot(data[:,1])\n",
    "    axs.grid(True)\n",
    "    axs.set_xlim(0, len(t))\n",
    "    axs.set_ylim(-y_scale, y_scale)\n",
    "    axs.set_xlabel(\"timestamp (every {})\".format(t_unit))\n",
    "    axs.set_ylabel(\"normalised readings\")\n",
    "    axs.set_title(\"{} dataset\\n(normalised by train mean {:.2f} and std {:.2f})\".format(dataset, train_m, train_std))\n",
    "    axs.legend(('data', 'train test set split', 'anomalies'))\n",
    "    \n",
    "    return t, readings_normalised"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example on ambient temperature series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcd6b1fcd50>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(idx_split[1]*np.ones(20), np.linspace(-y_scale,y_scale,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> Anomalies occur at:\n",
      "  timestamp #0: 2013-12-22 20:00:00\n",
      "  timestamp #1: 2014-04-13 09:00:00\n",
      "\n",
      "Original csv file contains (7267,) timestamps.\n",
      "Processed time series contain (7267,) readings.\n",
      "Anomaly indices are [3722, 6181]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([   1,    2,    3, ..., 7265, 7266, 7267]),\n",
       " 'hour',\n",
       " array([69.88083514, 71.22022706, 70.87780496, ..., 72.04656545,\n",
       "        71.82522648, 72.58408858]),\n",
       " [3722, 6181])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_data(\"ambient_temp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> Anomalies occur at:\n",
      "  timestamp #0: 2013-12-22 20:00:00\n",
      "  timestamp #1: 2014-04-13 09:00:00\n",
      "\n",
      "Original csv file contains (7267,) timestamps.\n",
      "Processed time series contain (7267,) readings.\n",
      "Anomaly indices are [3722, 6181]\n",
      "\n",
      "Training set mean is 72.04114695024849\n",
      "Training set std is 3.323831562717656\n",
      "Anomaly indices in the test set are [ 422 2881]\n",
      "\n",
      "Processed time series are not saved.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = 'ambient_temp'\n",
    "idx_split = [0,3300]\n",
    "\n",
    "t, readings_normalised = process_and_save_specified_dataset(dataset, idx_split)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MY CODE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## interrogating load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_folder='./NAB-known-anomaly/csv-files/'\n",
    "data_file = os.path.join(csv_folder, 'ambient_temperature_system_failure.csv')\n",
    "anomalies = ['2013-12-22 20:00:00', '2014-04-13 09:00:00']\n",
    "t_unit = 'hour'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> Anomalies occur at:\n",
      "  timestamp #0: 2013-12-22 20:00:00\n",
      "  timestamp #1: 2014-04-13 09:00:00\n"
     ]
    }
   ],
   "source": [
    "t = []\n",
    "readings = []\n",
    "idx_anomaly = []\n",
    "i = 0\n",
    "\n",
    "\n",
    "readCSV = csv.reader(open(data_file), delimiter=',')\n",
    "print(\"\\n--> Anomalies occur at:\")\n",
    "for row in readCSV:\n",
    "    if i > 0:\n",
    "        t.append(i)\n",
    "        readings.append(float(row[1]))\n",
    "        for j in range(len(anomalies)):\n",
    "            if row[0] == anomalies[j]:\n",
    "                idx_anomaly.append(i)\n",
    "                print(\"  timestamp #{}: {}\".format(j, row[0]))\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Original csv file contains (7267,) timestamps.\n",
      "Processed time series contain (7267,) readings.\n",
      "Anomaly indices are [3722, 6181]\n"
     ]
    }
   ],
   "source": [
    "t = np.asarray(t)\n",
    "readings = np.asarray(readings)\n",
    "print(\"\\nOriginal csv file contains {} timestamps.\".format(t.shape))\n",
    "print(\"Processed time series contain {} readings.\".format(readings.shape))\n",
    "print(\"Anomaly indices are {}\".format(idx_anomaly))\n",
    "    \n",
    "# return t, t_unit, readings, idx_anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    2,    3, ..., 7265, 7266, 7267])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = []\n",
    "readings = []\n",
    "idx_anomaly = []\n",
    "i = 0\n",
    "\n",
    "with open(data_file) as csvfile:\n",
    "    readCSV = csv.reader(csvfile, delimiter=',')\n",
    "    print(\"\\n--> Anomalies occur at:\")\n",
    "    for row in readCSV:\n",
    "        if i > 0:\n",
    "            t.append(i)\n",
    "            readings.append(float(row[1]))\n",
    "            for j in range(len(anomalies)):\n",
    "                if row[0] == anomalies[j]:\n",
    "                    idx_anomaly.append(i)\n",
    "                    print(\"  timestamp #{}: {}\".format(j, row[0]))\n",
    "        i = i + 1\n",
    "t = np.asarray(t)\n",
    "readings = np.asarray(readings)\n",
    "print(\"\\nOriginal csv file contains {} timestamps.\".format(t.shape))\n",
    "print(\"Processed time series contain {} readings.\".format(readings.shape))\n",
    "print(\"Anomaly indices are {}\".format(idx_anomaly))\n",
    "    \n",
    "# return t, t_unit, readings, idx_anomaly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## interogating process_and_save_specified_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> Anomalies occur at:\n",
      "  timestamp #0: 2013-12-22 20:00:00\n",
      "  timestamp #1: 2014-04-13 09:00:00\n",
      "\n",
      "Original csv file contains (7267,) timestamps.\n",
      "Processed time series contain (7267,) readings.\n",
      "Anomaly indices are [3722, 6181]\n"
     ]
    }
   ],
   "source": [
    "# This function plots a dataset with the train/test split and known anomalies\n",
    "# Relies on helper function load_data()\n",
    "\n",
    "dataset = 'ambient_temp'\n",
    "idx_split = [0,3300]\n",
    "\n",
    "y_scale=5\n",
    "save_file=False\n",
    "\n",
    "\n",
    "t, t_unit, readings, idx_anomaly = load_data(dataset)\n",
    "\n",
    "# split into training and test sets\n",
    "training = readings[idx_split[0]:idx_split[1]]\n",
    "t_train = t[idx_split[0]:idx_split[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalise by training mean and std \n",
    "train_m = np.mean(training)\n",
    "train_std = np.std(training)\n",
    "print(\"\\nTraining set mean is {}\".format(train_m))\n",
    "print(\"Training set std is {}\".format(train_std))\n",
    "readings_normalised = (readings - train_m) / train_std\n",
    "\n",
    "training = readings_normalised[idx_split[0]:idx_split[1]]\n",
    "if idx_split[0] == 0:\n",
    "    test = readings_normalised[idx_split[1]:]\n",
    "    t_test = t[idx_split[1]:] - idx_split[1]\n",
    "    idx_anomaly_test = np.asarray(idx_anomaly) - idx_split[1]\n",
    "else:\n",
    "    test = [readings_normalised[:idx_split[0]], readings_normalised[idx_split[1]:]]\n",
    "    t_test = [t[:idx_split[0]], t[idx_split[1]:] - idx_split[1]]\n",
    "    idx_anomaly_split = np.squeeze(np.argwhere(np.asarray(idx_anomaly)>idx_split[0]))\n",
    "    idx_anomaly_test = [np.asarray(idx_anomaly[:idx_anomaly_split[0]]), \n",
    "                        np.asarray(idx_anomaly[idx_anomaly_split[0]:]) - idx_split[1]]\n",
    "print(\"Anomaly indices in the test set are {}\".format(idx_anomaly_test))\n",
    "\n",
    "if save_file:\n",
    "    save_dir = './datasets/NAB-known-anomaly/'\n",
    "    np.savez(save_dir+dataset+'.npz', t=t, t_unit=t_unit, readings=readings, idx_anomaly=idx_anomaly,\n",
    "                idx_split=idx_split, training=training, test=test, train_m=train_m, train_std=train_std,\n",
    "                t_train=t_train, t_test=t_test, idx_anomaly_test=idx_anomaly_test)\n",
    "    print(\"\\nProcessed time series are saved at {}\".format(save_dir+dataset+'.npz'))\n",
    "else:\n",
    "    print(\"\\nProcessed time series are not saved.\")\n",
    "\n",
    "# plot the whole normalised sequence\n",
    "fig, axs = plt.subplots(1, 1, figsize=(18, 4), edgecolor='k')\n",
    "fig.subplots_adjust(hspace=.4, wspace=.4)\n",
    "# axs = axs.ravel()\n",
    "# for i in range(4):\n",
    "axs.plot(t, readings_normalised)\n",
    "if idx_split[0] == 0:\n",
    "    axs.plot(idx_split[1]*np.ones(20), np.linspace(-y_scale,y_scale,20), 'b--')\n",
    "else:\n",
    "    for i in range(2):\n",
    "        axs.plot(idx_split[i]*np.ones(20), np.linspace(-y_scale,y_scale,20), 'b--')\n",
    "for j in range(len(idx_anomaly)):\n",
    "    axs.plot(idx_anomaly[j]*np.ones(20), np.linspace(-y_scale,y_scale,20), 'r--')\n",
    "#     axs.plot(data[:,1])\n",
    "axs.grid(True)\n",
    "axs.set_xlim(0, len(t))\n",
    "axs.set_ylim(-y_scale, y_scale)\n",
    "axs.set_xlabel(\"timestamp (every {})\".format(t_unit))\n",
    "axs.set_ylabel(\"normalised readings\")\n",
    "axs.set_title(\"{} dataset\\n(normalised by train mean {:.2f} and std {:.2f})\".format(dataset, train_m, train_std))\n",
    "axs.legend(('data', 'train test set split', 'anomalies'))\n",
    "\n",
    "return t, readings_normalised"
   ]
  }
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